def getPointsPath(x1,y1,x2,y2,step,width,height,p1=1,p2=1):
# start with empty path
path=[]
lastpoint=(-1,-1)
# calculate straight line distance between coords
delta_x=x2-x1
delta_y=y2-y1
delta_p=p2-p1
h=math.hypot(abs(delta_x),abs(delta_y))
# if distance between is too small, just return coord 2
if h < step*2:
path.append((x2,y2,p2))
return path
# calculate intermediate coords
intermediate_points=numpy.arange(step,h,step)
for point in intermediate_points:
newx=int(x1+(delta_x*point/h))
newy=int(y1+(delta_y*point/h))
newp=p1+(delta_p*point/h)
# make sure coords fall in widht and height restrictions
if newx>=0 and newx<width and newy>=0 and newy<height:
# only add point if it was different from previous one
if newx!=lastpoint[0] or newy!=lastpoint[1]:
lastpoint=(newx,newy,newp)
path.append(lastpoint)
if x2>=0 and x2<width and y2>=0 and y2<height:
path.append((x2,y2,p2))
return path
def run_experiment(niter=100):
K = 100
results = []
for _ in xrange(niter):
mat = np.random.randn(K, K)
max_eigenvalue = np.abs(eigvals(mat).max())
results.append(max_eigenvalue)
return results
def getPointsPath(x1,y1,x2,y2,linestep,width,height,p1=1,p2=1): # start with a blank list path=[] lastpoint=(x1,y1) # calculate straight line distance between coords delta_x=x2-x1 delta_y=y2-y1 delta_p=p2-p1 h=math.hypot(abs(delta_x),abs(delta_y)) # calculate intermediate coords intermediate_points=numpy.arange(linestep,h,linestep) if len(intermediate_points)==0: return path pstep=delta_p/len(intermediate_points) newp=p1 for point in intermediate_points: newx=x1+(delta_x*point/h) newy=y1+(delta_y*point/h) newp=newp+pstep # make sure coords fall in widht and height restrictions if newx>=0 and newx<width and newy>=0 and newy<height: # make sure we don't skip a point #if step==0 int(newx)!=int(lastpoint[0]) and int(newy)!=int(lastpoint[1]): # print "skipped from point:", lastpoint, "to:", newx,newy # only add point if it was different from previous one #if int(newx)!=int(lastpoint[0]) or int(newy)!=int(lastpoint[1]): lastpoint=(newx,newy,newp) path.append(lastpoint) return path
import NumPy as np # Converting NumPy array to byte format byte_output = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]).tobytes() # Converting byte format back to NumPy array array_format = np.frombuffer(byte_output)
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
import NumPy as np
import logging
logger = tf.get_logger()
logger.setLevel(logging.ERROR)
celsius_q = np.array([-40, -10, 0, 8, 15, 22, 38], dtype=float)
farheneit_a = np.array([-40, 14, 32, 46, 59, 72, 100], dtype=float)
for i, c in enumerate(celsius_q):
print("{} degrees Celsius = {} degrees farenheit".format(c, farheneit_a[i]))
slope = 1
intercept = 1
learning_rate = 0.1
'''
for i in range(epochs):
curr_values = []
for c in celsius_q:
curr_values.append(slope*celsisu+slope)
for i in range(len(curr_values)):
error = curr_values[i]-celsius_q[i]
'''
# creating the underlying neural network to identify the relationships
IO = tf.keras.layers.Dense(units=1,input_shape=1)
import tensorflow as tf
import matplotlib.pyplot as plt
##sess = tf.InteractiveSession(config=tf.ConfigProto(log_device_placement=True))
##
##x = tf.constant([t for t in range(100)], shape=(1,100), dtype=tf.float32)
##y = tf.constant(4, dtype=tf.float32)
##
##out = tf.scalar_mul(y,x)
##
##
##
##sess.run(tf.global_variables_initializer())
# Creates a graph.
##with tf.device("/device:GPU:0"):
## a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
## b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
## c = tf.matmul(a, b)
### Creates a session with log_device_placement set to True.
##sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
### Runs the op.
##print(sess.run(c))
a = np.array([1, 2, 3, 4, 5, 6, -34])
b = np.divide(a, 2)
print(a, b)
a = np.array([1, 2, 3, 4, 5, 6, -34])
b = np.divide(a, 3)
print(a, b)
def print_NumPy():
x = np.arange(12, 38)
#_*_encoding:utf-8_*_ #!/usr/bin/env python import NumPy as np a = np.arange(15).reshape(3, 5) print a
########################################
########################################
# http://www.labri.fr/perso/nrougier/teaching/numpy.100/
########################################
# Exercise 1
# Import the NumPy package under the name np
import NumPy as np
# Exercise 2
# Print the NumPy version and the configuration
print(np.__version__)
np.show_config()
# Exercise 3
# Create a null vector of size 10
Z = np.zeros(10)
print(Z)
# Exercise 4
# How to get the documentation of the numpy add function
# from the command line
python -c "import numpy; numpy.info(numpy.add)"
# Exercise 5
# Create a null vector of size 10 but the fifth value which is 1
Z = np.zeros(10)
Z[4]=1
